{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:32:31Z","timestamp":1753885951710,"version":"3.41.2"},"reference-count":12,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2008,5,15]],"date-time":"2008-05-15T00:00:00Z","timestamp":1210809600000},"content-version":"vor","delay-in-days":135,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computer Networks and Communications"],"published-print":{"date-parts":[[2008,1]]},"abstract":"<jats:p>We propose a method for job migration policies by considering effective usage of global memory \nin addition to CPU load sharing in distributed systems. When a node is identified for lacking \nsufficient memory space to serve jobs, one or more jobs of the node will be migrated to remote \nnodes with low memory allocations. If the memory space is sufficiently large, the jobs will be \nscheduled by a CPU\u2010based load sharing policy. Following the principle of sharing both CPU and \nmemory resources, we present several load sharing alternatives. Our objective is to reduce the \nnumber of page faults caused by unbalanced memory allocations for jobs among distributed \nnodes, so that overall performance of a distributed system can be significantly improved. We have \nconducted trace\u2010driven simulations to compare CPU\u2010based load sharing policies with our policies. \nWe show that our load sharing policies not only improve performance of memory bound jobs, but \nalso maintain the same load sharing quality as the CPU\u2010based policies for CPU\u2010bound jobs. Regarding \nremote execution and preemptive migration strategies, our experiments indicate that a strategy \nselection in load sharing is dependent on the amount of memory demand of jobs, remote \nexecution is more effective for memory\u2010bound jobs, and preemptive migration is more effective for \nCPU\u2010bound jobs. Our CPU\u2010memory\u2010based policy using either high performance or high throughput \napproach and using the remote execution strategy performs the best for both CPU\u2010bound and \nmemory\u2010bound job in homogeneous networks of distributed environment.<\/jats:p>","DOI":"10.1155\/2008\/294106","type":"journal-article","created":{"date-parts":[[2008,5,15]],"date-time":"2008-05-15T14:19:33Z","timestamp":1210861173000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Generalized Load Sharing for Homogeneous Networks of Distributed Environment"],"prefix":"10.1155","volume":"2008","author":[{"given":"A.","family":"Satheesh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K. 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